#Note to run the animation you will have to instal Imagemagik from http://www.imagemagick.org/script/binary-releases.php
# when you install Imagemagik remember to select the "instal legacy files" option so convert.exe is included
#Set working directory where this script and the raw excel file are saved
setwd("C:/Users/Dan/Documents/GitHub/MelbournePropertyPrices")
### Install/load required packages
#List of R packages required for this analysis:
required_packages <- c("ggmap", "readr", "ggplot2", "dplyr", "readxl","tidyr",
"stringr", "gganimate","animation","ggiraph")
#Install required_packages:
new.packages <- required_packages[!(required_packages %in% installed.packages()[,"Package"])]
if(length(new.packages)) install.packages(new.packages)
#Load required_packages:
lapply(required_packages, require, character.only = TRUE)
## Loading required package: ggmap
## Loading required package: ggplot2
## Loading required package: readr
## Loading required package: dplyr
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
## Loading required package: readxl
## Loading required package: tidyr
## Loading required package: stringr
## Loading required package: gganimate
## Loading required package: animation
## Loading required package: ggiraph
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#Set decimal points and disable scientific notation
options(digits=3, scipen=999)
###Load Map
# Can download using get_map function:
# Melbourne <- get_map(location = 'Melbourne, Australia', zoom = 10, maptype="terrain")
# But I saved it to workind directory so can load it and work offline:
load("Melbourne.rda")
###Load Apartment price data
# Got this data here http://www.dtpli.vic.gov.au/property-and-land-titles/property-information/property-prices ...
# ...but saved it to workind directory so can load it and work offline
suburb_Apartment_2015<-read_excel("suburb_unit_2015.xls", col_names=F, skip = 3) %>%
na.omit() %>%
distinct() %>%
mutate(`Property Type`="Apartment")
#Load House Price Data and row bind it to Apartment price data
property_2005_2015<-read_excel("suburb_house_2015.xls", col_names=F, skip = 3) %>%
na.omit() %>%
distinct() %>%
mutate(`Property Type`="House") %>%
bind_rows(suburb_Apartment_2015)
rm(suburb_Apartment_2015)
#rename the columns
names(property_2005_2015)<-c("suburb", 2005:2015, "Prelim 2016", "Change 2014-2015 (%)", "Change 2005-2015 (%)", "Growth (%) PA 2005-2015","Property Type")
# assuming that "-" and "0" means data not available (NA),
property_2005_2015[property_2005_2015 == 0] <- NA
property_2005_2015[property_2005_2015 == "-"] <- NA
## Read in the lat/long data:
# Also saved it to workind directory so can load it and work offline:
lat_long<-read_csv("Australian_Post_Codes_Lat_Lon.csv") %>%
mutate(postcode=as.character(postcode)) %>%
distinct() %>%
select(-dc, -type)
## Parsed with column specification:
## cols(
## postcode = col_integer(),
## suburb = col_character(),
## state = col_character(),
## dc = col_character(),
## type = col_character(),
## lat = col_double(),
## lon = col_double()
## )
#create VIC only lat_long
VIC_lat_long<- lat_long %>%
filter(state=="VIC") %>%
select(-postcode) %>%
distinct() %>%
filter(suburb %in% property_2005_2015$suburb)
#Merge VIC_lat_long into property_2005_2015
property_2005_2015<- full_join(property_2005_2015, VIC_lat_long, by=c("suburb"))
rm(VIC_lat_long)
property_2005_2015 <- property_2005_2015 %>%
mutate(`Change 2014-2015 (%)` = as.numeric(`Change 2014-2015 (%)`),
`Change 2005-2015 (%)` = as.numeric(`Change 2005-2015 (%)`),
`Growth (%) PA 2005-2015` = as.numeric(`Growth (%) PA 2005-2015`))
#melt from wide to long format
property_2005_2015<-property_2005_2015 %>%
gather(key=Year, value=`Median Price ($)`, -suburb, -lon, -lat, -state, -`Property Type`, -`Change 2014-2015 (%)`, -`Change 2005-2015 (%)`, -`Growth (%) PA 2005-2015`) %>%
mutate(`Median Price ($)`=as.numeric(`Median Price ($)`))
Map
p1<-ggmap(Melbourne) +
geom_point(data = property_2005_2015,
aes(x =lon, y= lat, frame = Year, size=`Median Price ($)`,
colour = `Median Price ($)`), alpha=.75, shape="$") +
scale_colour_gradientn(colours=rainbow(5)) +
scale_radius (range = c(4, 14), trans = "identity", guide = "legend") +
facet_wrap(~`Property Type`) +
ggtitle("Median Melbourne Property Prices ($) from 2005-2016 \n")
p1 <- p1 + theme(aspect.ratio=1) +
theme(axis.title.x = element_blank(),
axis.text.x = element_blank(),
axis.title.x = element_blank(),
axis.ticks.x=element_blank(),
axis.text.y = element_blank(),
axis.title.y = element_blank(),
axis.ticks.y=element_blank(),
legend.title = element_text(size=12, face="bold"),
legend.text = element_text(size = 12, face = "bold"),
strip.text.x = element_text(size=12, face="bold"),
plot.title = element_text(size = 14, face = "bold"),
legend.position="right")
#Save the .gif to working directory
# gg_animate(p1, 'Melbourne.gif', ani.width = 1000, ani.height = 600, interval = 0.75)
#show the gif in the graphics pannel
# gg_animate(p1)
Change Map
p2<-ggmap(Melbourne, maprange=TRUE, extent = "normal", base_layer = ggplot(data = property_2005_2015, aes(x =lon, y= lat))) +
geom_point_interactive(data = property_2005_2015,
aes(x =lon, y= lat, size= `Change 2005-2015 (%)`,
colour = `Change 2005-2015 (%)`, tooltip=suburb, data_id = suburb),
alpha=.5) +
scale_colour_gradientn(colours=rainbow(5)) +
scale_radius (range = c(1, 6), trans = "identity", guide = "legend") +
facet_wrap(~`Property Type`) +
ggtitle("Change in Median Melbourne \n Property Prices ($) from 2005-2015 \n") +
theme(axis.title.x = element_blank(),
axis.text.x = element_blank(),
axis.title.x = element_blank(),
axis.ticks.x=element_blank(),
axis.text.y = element_blank(),
axis.title.y = element_blank(),
axis.ticks.y=element_blank(),
legend.title = element_text(face="bold"),
legend.text = element_text(face = "bold"),
strip.text.x = element_text(face="bold"),
plot.title = element_text(face = "bold"))
ggiraph(code = {print(p2)}, zoom_max = 5,
tooltip_offx = 20, tooltip_offy = -10,
hover_css = "fill:black;",
tooltip_opacity = 0.7)
# ggmap(Melbourne) +
# geom_point(data = property_2005_2015,
# aes(x =lon, y= lat, size=`Change ($)`,
# colour = `Change ($)`), alpha=.75, shape="$") +
# scale_colour_gradientn(colours=rainbow(5)) +
# scale_radius (range = c(1, 8), trans = "identity", guide = "legend") +
# facet_wrap(~`Property Type`) +
# ggtitle("Change in Median Melbourne Property Prices ($) from 2005-2015 \n")
p3<-ggmap(Melbourne) +
geom_point(data = property_2005_2015,
aes(x =lon, y= lat, size=`Change 2005-2015 (%)`,
colour = `Change 2005-2015 (%)`), alpha=.5, shape="%") +
scale_colour_gradientn(colours=rainbow(5)) +
scale_radius (range = c(1, 6), trans = "identity", guide = "legend") +
facet_wrap(~`Property Type`) +
ggtitle("% Change in Median Melbourne \n Property Prices ($) from 2005-2016 \n")
p3 <- p3 + theme(aspect.ratio=1) +
theme(axis.title.x = element_blank(),
axis.text.x = element_blank(),
axis.title.x = element_blank(),
axis.ticks.x=element_blank(),
axis.text.y = element_blank(),
axis.title.y = element_blank(),
axis.ticks.y=element_blank(),
legend.title = element_text(size=12, face="bold"),
legend.text = element_text(size = 12, face = "bold"),
strip.text.x = element_text(size=12, face="bold"),
plot.title = element_text(size = 14, face = "bold"),
legend.position="right")
p3
p4<-ggmap(Melbourne) +
geom_point(data = property_2005_2015,
aes(x =lon, y= lat, size=`Growth (%) PA 2005-2015`,
colour = `Growth (%) PA 2005-2015`), alpha=.5) +
scale_colour_gradientn(colours=rainbow(5)) +
scale_radius (range = c(1, 6), trans = "identity", guide = "legend") +
facet_wrap(~`Property Type`) +
ggtitle("Growth (%) per annum in Median Melbourne \n Property Prices ($) from 2005-2016 \n")
p4 <- p4 + theme(aspect.ratio=1) +
theme(axis.title.x = element_blank(),
axis.text.x = element_blank(),
axis.title.x = element_blank(),
axis.ticks.x=element_blank(),
axis.text.y = element_blank(),
axis.title.y = element_blank(),
axis.ticks.y=element_blank(),
legend.title = element_text(size=12, face="bold"),
legend.text = element_text(size = 12, face = "bold"),
strip.text.x = element_text(size=12, face="bold"),
plot.title = element_text(size = 14, face = "bold"),
legend.position="right")
p4
